The present invention relates to vision systems. More in particular it relates to real-time object tracking in vision systems using mean-shift iterations.
The classic formulation of the tracking problem using mean-shift iterations encodes spatial information very loosely (i.e. using radially symmetric kernels). A problem with such a formulation is that it becomes easy for the tracker to get confused with other objects having the same feature distribution but different spatial configurations of features. Subsequent approaches have addressed this issue but not to the degree of generality required for tracking specific classes of objects (e.g. humans).
A key issue is to have a tracker that encodes the spatial configuration of features along with their density and yet retains robustness to spatial deformations and feature density variations.
Accordingly methods providing improved tracking performance in using mean-shift iterations are required.